Results 61 to 70 of about 238,473 (148)
Dynamic Data Mining: Methodology and Algorithms
, 2011 Supervised data stream mining has become an important and challenging data mining task in modern organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples and time-critical analysis constraints; (2) concept ...
Deng, Xiong, Deng, Xiong
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Knowledge Discovery in Data Mining and Massive Data Mining [PDF]
, 2013 Knowledge discovery is a process of non trivial extraction of previously unknown and presently useful information. The rapid advancement of the technology resulted in the increasing rate of data distributions. The data generated from mobile applications,
Malini, M.Patil., Srimani, P.K.
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Fast and Accurate Mining of Correlated Heavy Hitters
, 2017 The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to solve it.
Cafaro, Massimo +2 more
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Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model [PDF]
Mehran University Research Journal of Engineering and Technology, 2016 EPs (Extracting Frequent Patterns) from the continuous transactional data streams is a challenging and critical task in some of the applications, such as web mining, data analysis and retail market, prediction and network monitoring, or analysis of ...
QAISAR JAVAID +4 more
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Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform
EAI Endorsed Transactions on Energy Web, 2018 This paper provides an improved parallel data processing in Big Data mining using ClowdFlows platform. The big data processing involves an improvement in Proportional Integral Derivative (PID) controller using Reinforcement Adaptive Learning (RAL).
N. Yuvaraj +3 more
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Constraint-based discriminative dimension selection for high-dimensional stream clustering
IJAIN (International Journal of Advances in Intelligent Informatics), 2018 Clustering data streams is one of active research topic in data mining. However, runtime of the existing stream clustering algorithms increases and their performance drop in the face of large number of dimensions.
Kitsana Waiyamai, Thanapat Kangkachit
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On Graph Stream Clustering with Side Information
, 2013 Graph clustering becomes an important problem due to emerging applications involving the web, social networks and bio-informatics. Recently, many such applications generate data in the form of streams.
Yu, Philip S., Zhao, Yuchen
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IEEE AccessA user model (UM) is a representation of the characteristics, behaviors, and preferences of a user that a computer system utilizes to provide the user with personalized experiences.
Maria Yesenia Zavaleta-Sanchez +4 more
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Sustainability-integrated value stream mapping with process mining
Production and Manufacturing Research: An Open Access JournalValue stream mapping is a well-established tool for analyzing and optimizing value streams in production. In its conventional form, it requires a high level of manual effort and is often inefficient in volatile and high-variance environments. The idea of
Julia Horsthofer-Rauch +6 more
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rEMM: Extensible Markov Model for Data Stream Clustering in R [PDF]
Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream
Margaret H. Dunham, Michael Hahsler
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